1. Field of the Invention
The present invention concerns a method for generation of magnetic resonance images of an examination subject. The invention can particularly (but not exclusively) be used in contrast agent-intensified 3D MR angiography.
2. Description of the Prior Art
In contrast agent-intensified 3D MR angiography, MR data of an examination subject or patient are acquired while the subject or patient is injected with contrast agent. It is typically sought to acquire the MR data such that the contrast agent is acquired in its arterial phase, meaning given the passage of the contrast agent through the arteries before the return flow in the veins. A high spatial resolution is required in order to image small vessels with these MR angiography methods. In some cases it is also desirable to obtain dynamic data of the contrast agent course, so a number of successive 3D data sets are acquired with which the bolus course of the contrast agent can be detected. However, a high spatial resolution and a high temporal resolution are contradictory requirements since more data points must be acquired for a high spatial resolution than for a lower spatial resolution.
Magnetic field gradients in chronological sequence and radio-frequency pulses for excitation of the nuclear spins are used for generation of magnetic resonance images. A mathematical arrangement known as k-space or Fourier space is filled with raw data bit by bit to generate MR images. Moreover, for many small questions it is desirable to have 3D volume data sets that are well-resolved spatially. In MR imaging a 3D data set can also be generated using multi-slice techniques, but the resolution is typically ten times poorer in the third dimension (i.e., in the slice direction) than in the plane. To acquire three-dimensional volume data sets with approximately isotropic resolution, individual slices are not selectively excited with RF pulses and gradient switchings and successively measured, rather the entire three-dimensional volume is excited and the spatial resolution in the third dimension is achieved by an additional gradient, known as the phase-coding gradient. Three-dimensional image data sets are generated from these three-dimensional volume data sets or raw data sets, for example with a Fourier transformation. The extent of the introduced k-space range reflects the spatial resolution achieved in image space, while the interval of adjacent data points reciprocally correlates with the achieved image section or FOV (field of view).
Various methods for shortening the acquisition times in MR imaging are known in the prior art.
For example, in methods using a “partially parallel acquisition” a smaller set of k-space data than is required for the image calculation is acquired and the MR signal is acquired simultaneously with a number of individual acquisition coils. A complete spatial coding can be achieved with special reconstruction algorithms using spatial information introduced from the coil geometry. The methods described in the literature are for the most part divided according to whether the additional calculation steps required for this occur in k-space or in image space. In methods such as SENSE (“Sensitivity Encodings”, Magn. Reson. Med. 42: 952-62) or PILS (“Partially Parallel Imaging with Localized Sensitivities”, Magn. Reson. Med. 44: 602-9), for example, a determination of the sensitivity of the sensitivity profiles of the coils is implemented first and a deconvolution of the image in image space is subsequently implemented. Given methods such as Auto-SMASH (“Simultaneous Acquisition of Spatial Harmonics”, Magn. Reson. Med. 425: 1066-74) or GRAPPA (“GeneRelized [sic] Autocalibrating Partially Parallel Acquisitions”, Magn. Reson. Med. 47: 1202-10), missing k-space data are calculated using additional auto-calibration data. All of these methods are summarized in the following under the term PPA (“partially parallel acquisition”).
A general shortening of the image acquisition time can be achieved using partial Fourier techniques given which the scanned region of k-space lies asymmetrically around the center such that outer regions are not acquired. Use is made of the fact that k-space exhibits point symmetry around the center in the ideal case. The missing regions can simply be completed by filling with zeroes (“zero filling”) or using more elaborate algorithms such as, for example, POCS (“projection onto convex sets”), homodyne detection or Margosian method (see Magn. Reson. Med. 30: 51-9).
Furthermore, techniques are known that make use of the fact that the most information about the image contrast is contained in the center of k-space (see Proceedings SMRM 1992, Nr. 4236, Nr. 1138 and Nr. 1139). In these techniques (known as keyhole techniques) the entirety of k-space is first acquired, and a dynamic image series is subsequently acquired in which only the central region of k-space is acquired. The outer k-space region is not acquired in the dynamic series but rather is respectively supplemented with the data of the full exposure. Since the high frequency information and therewith the high spatial resolutions are contained in the outer k-space regions, this entails a reduced spatial resolution of the dynamic information and can lead to edge artifacts.
Furthermore, methods are known in which k-space is subdivided into different segments acquired in succession. For example, the segment A can encompass the odd k-space lines and the segment B can encompass the even k-space lines. The acquisition pattern can, for example, be ABABAB (see Frederikson et al., Proceedings SMR 1995, Nr. 197). Images are generated from a k-space data set that contains adjacent segments (such as, for example, AB and BA) such that each segment is used for the reconstruction of more than one image, allowing the temporal resolution in the reconstructed images to be improved.
A method in which the middle region of k-space is acquired with a higher temporal rate than the outer k-space regions is known from U.S. Pat. No. 5,713,358. For example, a middle k-space region A and peripheral regions B and C are differentiated, and the acquisition pattern can be ABACABAC. Images are calculated, for example, from each segment A with data of temporally adjacent segments B and C.
A further method with which the acquisition time of an MR data set can be shortened is proposed in United States Patent Application Publication No. 2003/0080737 by sampling k-space with variable sampling density. There are various methods for the image reconstruction of such data acquired in a non-Cartesian manner. A direct Fourier transformation is possible but is linked with a long computation duration. For the most part a transformation onto a Cartesian grid (“regridding”) is implemented first, wherein a compensation of the variable scan density is normally important. The image reconstruction can subsequently ensue by means of a standard FT. If such an under-sampling is also associated with a limited image quality, acquisition techniques with variable density are also described as advantageous in connection with PPA methods.
A method in which a number of three-dimensional volume data sets with non-constant density are acquired is known from United States Patent Application Publication No. 2002/01563641, wherein three-dimensional image data sets are generated on the basis of assembled volume data sets in k-space.
A method in which k-space is acquired three-dimensionally in a spiral shape is known from United States Patent Application Publication No. 2006/0062731.
Furthermore, a method is known from U.S. Pat. No. 6,487,435, which method is based on projection reconstruction acquisition methods of k-space. Here k-space is not read out with parallel readout lines, but instead each readout line proceeds through the k-space middle point. Since the k-space center is sampled with higher density than the periphery in this manner, under the circumstances an image can be calculated from fewer acquisition steps than are required for the complete measurement of corresponding Cartesian k-space. In this case a regridding with subsequent Fourier transformation would also preferably be implemented first for image calculation. Such a method with three-dimensional, radially-arranged data has the disadvantages that it can be prone to image artifacts and that a long time is typically required for the image reconstruction.